Most SISR works [1]}, [2]}, [3]}, apply Gaussian smoothing using a fixed standard deviation on the training images, thus training the models in more difficult conditions. However, we believe that using a fixed standard deviation can harm the performance, as deep models tend to overfit to the training data. Different from the standard methodology, each time we apply smoothing on a training image, we chose a different standard deviation, randomly. This simple change improves the generalization capacity of our model, yielding better performance at test time.
